
from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__	,       __magic_name__								=			analyze_text(A_   )
  __magic_name__								=			list(""" """ + ascii_lowercase   )
  # what is our total sum of probabilities.
  __magic_name__								=			sum(single_char_strings.values()   )

  # one length string
  __magic_name__								=			0
  # for each alpha we go in our dict and if it is in it we calculate entropy
  for ch in my_alphas:
    if ch in single_char_strings:
      __magic_name__								=			single_char_strings[ch]
      __magic_name__								=			my_str / all_sum
      my_fir_sum += prob * math.loga(A_   )  # entropy formula.

    # print entropy
  print(f'''{round(-1 * my_fir_sum   ):.1f}'''   )

  # two len string
  __magic_name__								=			sum(two_char_strings.values()   )
  __magic_name__								=			0
  # for each alpha (two in size) calculate entropy.
  for cha in my_alphas:
    for cha in my_alphas:
      __magic_name__								=			cha + cha
      if sequence in two_char_strings:
        __magic_name__								=			two_char_strings[sequence]
        __magic_name__								=			int(A_   ) / all_sum
        my_sec_sum += prob * math.loga(A_   )

    # print second entropy
  print(f'''{round(-1 * my_sec_sum   ):.1f}'''   )

  # print the difference between them
  print(f'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)   ):.1f}'''   )

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			Counter()  # type: ignore
  __magic_name__								=			Counter()  # type: ignore
  single_char_strings[text[-1]] += 1

  # first case when we have space at start.
  two_char_strings[" " + text[0]] += 1
  for i in range(0,	len(A_   ) - 1   ):
    single_char_strings[text[i]] += 1
    two_char_strings[text[i : i + 2]] += 1
  return single_char_strings, two_char_strings

def a__       (			):






  '''simple docstring'''





  import doctest

  doctest.testmod()
  # text = (
  #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
  #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
  #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
  #     "beloved evident through no service elderly is. Blind there if every no so "
  #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
  #     "of message cottage windows do besides against uncivil.  Delightful "
  #     "unreserved impossible few estimating men favourable see entreaties. She "
  #     "propriety immediate was improving. He or entrance humoured likewise "
  #     "moderate. Much nor game son say feel. Fat make met can must form into "
  #     "gate. Me we offending prevailed discovery. "
  # )

  # calculate_prob(text)


if __name__ == "__main__":
    main()



import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD




class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						True
  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=UpperCamelCase__     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.out_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.out_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				only_cross_attention=self.only_cross_attention     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions

    if self.add_downsample:
      __magic_name__								=			FlaxDownsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					List[Any]     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Optional[int]=True       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			()

    for resnet, attn in zip(self.resnets     ,				self.attentions       ):
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      output_states += (hidden_states,)

    if self.add_downsample:
      __magic_name__								=			self.downsamplers_a(UpperCamelCase__       )
      output_states += (hidden_states,)

    return hidden_states, output_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						True
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					str       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=UpperCamelCase__     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )
    __magic_name__								=			resnets

    if self.add_downsample:
      __magic_name__								=			FlaxDownsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					str     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					int=True       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			()

    for resnet in self.resnets:
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      output_states += (hidden_states,)

    if self.add_downsample:
      __magic_name__								=			self.downsamplers_a(UpperCamelCase__       )
      output_states += (hidden_states,)

    return hidden_states, output_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						True
  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if (i == self.num_layers - 1) else self.out_channels
      __magic_name__								=			self.prev_output_channel if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=resnet_in_channels + res_skip_channels     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.out_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.out_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				only_cross_attention=self.only_cross_attention     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions

    if self.add_upsample:
      __magic_name__								=			FlaxUpsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					Dict=True       )     ->      Union[str, Any]:







    """simple docstring"""






    for resnet, attn in zip(self.resnets     ,				self.attentions       ):
      # pop res hidden states
      __magic_name__								=			res_hidden_states_tuple[-1]
      __magic_name__								=			res_hidden_states_tuple[:-1]
      __magic_name__								=			jnp.concatenate((hidden_states, res_hidden_states)     ,				axis=-1       )

      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    if self.add_upsample:
      __magic_name__								=			self.upsamplers_a(UpperCamelCase__       )

    return hidden_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						True
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					List[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if (i == self.num_layers - 1) else self.out_channels
      __magic_name__								=			self.prev_output_channel if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=resnet_in_channels + res_skip_channels     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

    __magic_name__								=			resnets

    if self.add_upsample:
      __magic_name__								=			FlaxUpsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					List[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					str=True       )     ->      Dict:







    """simple docstring"""






    for resnet in self.resnets:
      # pop res hidden states
      __magic_name__								=			res_hidden_states_tuple[-1]
      __magic_name__								=			res_hidden_states_tuple[:-1]
      __magic_name__								=			jnp.concatenate((hidden_states, res_hidden_states)     ,				axis=-1       )

      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    if self.add_upsample:
      __magic_name__								=			self.upsamplers_a(UpperCamelCase__       )

    return hidden_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					List[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[
        FlaxResnetBlockaD(
            in_channels=self.in_channels     ,				out_channels=self.in_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
    ]

    __magic_name__								=			[]

    for _ in range(self.num_layers       ):
      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.in_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.in_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=self.in_channels     ,				out_channels=self.in_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions





  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					int=True       )     ->      str:







    """simple docstring"""






    __magic_name__								=			self.resnets[0](UpperCamelCase__     ,				UpperCamelCase__       )
    for attn, resnet in zip(self.attentions     ,				self.resnets[1:]       ):
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    return hidden_states

